The present invention is generally directed to geolocation functionality of a mobile device in a network. In particular, it provides a system and a method suitable for managing a geofence associated with the geolocation of the mobile device.
Cellular phones are increasingly becoming used for a variety of rich media purposes, such as taking photographs, playing music and performing more complex computational tasks. As such, companies are now viewing the mobile platform as a medium to provide users will value-added targeted services. Targeting can be based on the context of the user themselves, and also be location dependent. The context of the user could include attributes of the user, their likes, status, habits, what the user is currently doing, a context of the mobile device and network, battery state, radio signal state, location in three dimensions, movement, mode of transport etc.
The continuing growth of mobile data traffic, smart phone penetration and the realization of the “Internet of things” have placed requirements upon mobile network infrastructure that the infrastructure was not originally designed to accommodate. Adding more intelligence at the edge of mobile networks allow operators to optimize their infrastructure to deal with unprecedented amounts of traffic, to rapidly deliver innovative features to accelerate data services and to enable a completely unique mobile broadband experience that can be directly translated into values.
One example of recent edge of network functionality is provided by IBM® WebSphere® Application Service Platform (ASPN). This leverages IBM's ASPN edge of network capability which is described here:
http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=WH&infotype=SA&appname=SWGE_WS_WS_USEN&htmlfid=WSW14201USEN&attachment=WSW14201USEN PDF
(Retrieved from the Internet on 22 Jun. 2013).
The business owners of mobile device applications (app) would like to provide a service to a user of that app when the user enters a particular small and well defined geographic area. For example, a voucher could be provided to a user when the user enters a shop.
One problem is that users routinely disable location based apps to conserve device battery life. Therefore, existing location based apps are ineffective in relation to geofence functionality.
A geofence is a virtual perimeter for a real-world geographic area. A geofence is active when a specified user or device enters (alternatively leaves) the area.
Geofencing APIs provide means to sense or generate an event for an active geofence. Therefore, contextual applications and use cases can be enabled. Known geofence APIs include those of Google, which operates in the device; and of Sprint, which operates in the network.
A solution which takes an action when a user enters a geographic area is known as a geofence based solution.
A practical geofence solution requires the coupling of two technologies: i) a geolocation (finding where the mobile device is) and ii) geofencing determining whether the mobile device is within a defined area).
Using today's technology there are two ways to do geolocation: mobile device based geolocation; and network based geolocation.
In mobile device based geolocation, the geofencing implementation must constantly monitor the mobile device location via one of the mobile device based location methods which include, for example, Global Positioning System (GPS), Monitoring WiFi MaC addresses, cell identifiers, or cell triangulation. All of these methods require the device to be actively processing information all the time and many require power-hungry hardware to be turned on in the device. These tend to drain the battery very quickly. As these techniques drain the battery very quickly users generally will not leave these types of applications running for long periods. Typically the users will try to remember to turn them on when they might be useful, but typically this means that the applications are not running at the point in time where a useful solution could be provided.
Recently technology has been implemented to try to reduce the battery usage by implementing a two tier system which uses course-scale location to fix an approximate location and then enables the GPS only when fine-scale location is necessary. This technology however still uses battery on the mobile device and as a result consumers are reluctant to enable it. Other mobile device application technologies use one processor on the mobile device to fix a coarse granularity of location, and another processor to fix a fine granularity of location.
U.S. Pat. No. 8,396,485 B2 (Grainger, M. et al. “Beacon-based Geofencing”, Mar. 12, 2013) discloses a baseband subsystem that monitors a coarse location of a mobile device. The application subsystem then carries a fine grained geolocation.
Alternatively, network based geolocation can be used. Traditional network based geolocation uses information extracted from the network control plane to determine the location of the device. Such information includes call data records (CDRs) indicating cell handover or routing area handover. However, such techniques are typically capable of locating a user to a granularity of at best one cell in a cellular telecommunications system. Since cells can be several kilometers in diameter this is quite a low granularity system.
Recently new techniques have emerged for calculating location in a cellular network. One such technique, sometimes termed “radio frequency pattern matching” (RFPM) involves intercepting the regular Network Management Reports (NMRs) which are sent from the mobile device to the network. These NMRs contain detailed information about the signal strength from multiple transmitters as seen by the mobile device. The RFPM technique creates a database of many such observations correlated with the known true position of the mobile device that created the NMR. The true position in this case is generated from GPS or some other highly accurate technique. Once this database of observations has been built up, it is possible to determine the location of a mobile device in two or three dimensions by comparing the NMRs it produces to the database of previously collected NMRs. Such correlations lead to highly accurate position fixes, particularly indoors or in dense urban environments which exhibit characteristic reflections and attenuation produced by walls and buildings. The technique is made even more accurate by tracking the movement of the user over time and by building up a large database showing NMRs from many different types of device in many weather and radio conditions.
There are drawbacks with existing geofencing solutions. Existing device based solutions tend to drain the device battery and typically cannot use high precision GPS when operating inside buildings. Existing network based geofencing implementations use low granularity (that is course-scale) location. Cell-id based geofencing will locate a user to the area of a cell which may have a radius of 1 to 5 km. There is no solution today which combines fine scale network based geolocation with geofencing to create a geofence with a diameter of a few tens of meters.
Moreover, the most obvious architecture to create such a system would be to connect fine scale geolocation (Radio Frequency Pattern Matching) with geolocation on a large computer server or cluster at the core of the network. The problem with this obvious solution is that tracking the real time location, second by second of upwards of a million users in a network of up to 50,000 cell towers and determining whether each user has triggered one of the geofences that are active for that user is a huge compute task.
Therefore, there is a need in the art to address the aforementioned problem.